code: def forward(self, x):
      x = super().forward(x)
      if self.binary_classification:
          output = torch.sigmoid(x)
          output = torch.cat([1 - output, output], dim=1)
      else:
          output = torch.softmax(x, dim=1)
  
      if self.perform_class_select:
          return (
              torch.index_select(
                  self.classes, 0, torch.argmax(output, dim=1)
              ),
              output,
          )
      else:
          return torch.argmax(output, dim=1), output
  
file_path: hummingbird/hummingbird/ml/operator_converters/_mlp_implementations.py
imports: import torch
  from ._physical_operator import PhysicalOperator
label_desc: Use image node_id 2 for calling the MLPClassificationModel obj's underlying member method code with example usage: obj.forward(x) and returns: torch,  output, torch,  output